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            "Development Status :: 3 - Alpha",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
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            "Programming Language :: Python :: 3.5",
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        "description": "cortanaanalytics\r\n================\r\n\r\nThis a Python library for using Microsoft Azure Datamarket and Cortana Analytics Services.\r\n\r\n\r\nInstallation\r\n------------\r\n\r\nTo install, use pip:\r\n\r\n.. code:: shell\r\n\r\n    pip install cortanaanalytics\r\n\r\nYou can also get the development versions directly from the GitHub repo: http://github.com/crwilcox/cortanaanalytics\r\n\r\nGetting Started\r\n---------------\r\nCortana Analytics has many different packages. Please look at each section for the library you are interested in.\r\n\r\nAlso, you will need `obtain an access key <https://datamarket.azure.com/account/keys>`__ from the Azure Datamarket and subscribe to the service you wish to use.\r\n\r\n\r\nText Analytics\r\n--------------\r\nhttps://datamarket.azure.com/dataset/amla/text-analytics\r\n\r\n.. code:: python\r\n\r\n    from cortanaanalytics.textanalytics import TextAnalytics\r\n\r\n    key = '1abCdEFGh/ijKlmN/opq234r56st/UvWXYZabCD7EF8='\r\n    ta = TextAnalytics(key)\r\n\r\n    score = ta.get_sentiment(\"hello world\")\r\n\r\n    scores = ta.get_sentiment_batch([{\"Text\":\"hello world\", \"Id\":0}, {\"Text\":\"hello world again\", \"Id\":2}])\r\n\r\nRecommendations\r\n---------------\r\nhttps://datamarket.azure.com/dataset/amla/recommendations\r\n\r\n.. code:: python\r\n\r\n    from cortanaanalytics.recommendations import Recommendations\r\n\r\n    email = 'email@outlook.com'\r\n    key = '1abCdEFGh/ijKlmN/opq234r56st/UvWXYZabCD7EF8='\r\n    rs = Recommendations(email, key)\r\n\r\n    # create model\r\n    model_id = rs.create_model('groceries' + datetime.now().strftime('%Y%m%d%H%M%S'))\r\n\r\n    # import item catalog\r\n    catalog_path = os.path.join('app', 'management', 'commands', 'catalog.csv')\r\n    rs.import_file(model_id, catalog_path, Uris.import_catalog)\r\n\r\n    # import usage information\r\n    transactions_path = os.path.join('app', 'management', 'commands', 'transactions.csv')\r\n    rs.import_file(model_id, transactions_path, Uris.import_usage)\r\n\r\n    # build model\r\n    build_id = rs.build_fbt_model(model_id)\r\n    status = rs.wait_for_build(model_id, build_id)\r\n\r\n    if status != BuildStatus.success:\r\n        print('Unsuccessful in building the model, failing now.')\r\n        return\r\n\r\n    # update model active build (not needed unless you are rebuilding)\r\n    rs.update_model(model_id, None, build_id)\r\n\r\n    print('Built a model. Model ID:{} Build ID:{}'.format(model_id, build_id))\r\n\r\nAnomaly Detection\r\n-----------------\r\nhttps://datamarket.azure.com/dataset/aml_labs/anomalydetection\r\n\r\n.. code:: python\r\n\r\n    from cortanaanalytics.anomalydetection import AnomalyDetection  \r\n\r\n    key = '1abCdEFGh/ijKlmN/opq234r56st/UvWXYZabCD7EF8='\r\n    ad = AnomalyDetection(key)\r\n\r\n    data = [\r\n                (datetime(2014, 9, 21, 11, 5, 0), 3),\r\n                (datetime(2014, 9, 21, 11, 10, 0), 9.09),\r\n                (datetime(2014, 9, 21, 11, 15, 0), 0)\r\n           ]\r\n    result = ad.score(test_data)\r\n\r\nor you can also use strings\r\n\r\n.. code:: python\r\n\r\n    from cortanaanalytics.anomalydetection import AnomalyDetection  \r\n\r\n    key = '1abCdEFGh/ijKlmN/opq234r56st/UvWXYZabCD7EF8='\r\n    ad = AnomalyDetection(key)\r\n\r\n    data = \"9/21/2014 11:05:00 AM=3;9/21/2014 11:10:00 AM=9.09;9/21/2014 11:15:00 AM=0;\"\r\n    params = \"SpikeDetector.TukeyThresh=3; SpikeDetector.ZscoreThresh=3\"\r\n    result = ad.score_raw(data, params)",
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